parallel-library/vendor/singledispatch/PKG-INFO

Metadata-Version: 1.1
Name: singledispatch
Version: 3.4.0.3
Summary: This library brings functools.singledispatch from Python 3.4 to Python 2.6-3.3.
Home-page: http://docs.python.org/3/library/functools.html#functools.singledispatch
Author: Łukasz Langa
Author-email: lukasz@langa.pl
License: MIT
Description: ==============
        singledispatch
        ==============
        
        `PEP 443 <http://www.python.org/dev/peps/pep-0443/>`_ proposed to expose
        a mechanism in the ``functools`` standard library module in Python 3.4
        that provides a simple form of generic programming known as
        single-dispatch generic functions.
        
        This library is a backport of this functionality to Python 2.6 - 3.3.
        
        To define a generic function, decorate it with the ``@singledispatch``
        decorator. Note that the dispatch happens on the type of the first
        argument, create your function accordingly::
        
          >>> from singledispatch import singledispatch
          >>> @singledispatch
          ... def fun(arg, verbose=False):
          ...     if verbose:
          ...         print("Let me just say,", end=" ")
          ...     print(arg)
        
        To add overloaded implementations to the function, use the
        ``register()`` attribute of the generic function. It is a decorator,
        taking a type parameter and decorating a function implementing the
        operation for that type::
        
          >>> @fun.register(int)
          ... def _(arg, verbose=False):
          ...     if verbose:
          ...         print("Strength in numbers, eh?", end=" ")
          ...     print(arg)
          ...
          >>> @fun.register(list)
          ... def _(arg, verbose=False):
          ...     if verbose:
          ...         print("Enumerate this:")
          ...     for i, elem in enumerate(arg):
          ...         print(i, elem)
        
        To enable registering lambdas and pre-existing functions, the
        ``register()`` attribute can be used in a functional form::
        
          >>> def nothing(arg, verbose=False):
          ...     print("Nothing.")
          ...
          >>> fun.register(type(None), nothing)
        
        The ``register()`` attribute returns the undecorated function which
        enables decorator stacking, pickling, as well as creating unit tests for
        each variant independently::
        
          >>> @fun.register(float)
          ... @fun.register(Decimal)
          ... def fun_num(arg, verbose=False):
          ...     if verbose:
          ...         print("Half of your number:", end=" ")
          ...     print(arg / 2)
          ...
          >>> fun_num is fun
          False
        
        When called, the generic function dispatches on the type of the first
        argument::
        
          >>> fun("Hello, world.")
          Hello, world.
          >>> fun("test.", verbose=True)
          Let me just say, test.
          >>> fun(42, verbose=True)
          Strength in numbers, eh? 42
          >>> fun(['spam', 'spam', 'eggs', 'spam'], verbose=True)
          Enumerate this:
          0 spam
          1 spam
          2 eggs
          3 spam
          >>> fun(None)
          Nothing.
          >>> fun(1.23)
          0.615
        
        Where there is no registered implementation for a specific type, its
        method resolution order is used to find a more generic implementation.
        The original function decorated with ``@singledispatch`` is registered
        for the base ``object`` type, which means it is used if no better
        implementation is found.
        
        To check which implementation will the generic function choose for
        a given type, use the ``dispatch()`` attribute::
        
          >>> fun.dispatch(float)
          <function fun_num at 0x1035a2840>
          >>> fun.dispatch(dict)    # note: default implementation
          <function fun at 0x103fe0000>
        
        To access all registered implementations, use the read-only ``registry``
        attribute::
        
          >>> fun.registry.keys()
          dict_keys([<class 'NoneType'>, <class 'int'>, <class 'object'>,
                    <class 'decimal.Decimal'>, <class 'list'>,
                    <class 'float'>])
          >>> fun.registry[float]
          <function fun_num at 0x1035a2840>
          >>> fun.registry[object]
          <function fun at 0x103fe0000>
        
        The vanilla documentation is available at
        http://docs.python.org/3/library/functools.html#functools.singledispatch.
        
        
        Versioning
        ----------
        
        This backport is intended to keep 100% compatibility with the vanilla
        release in Python 3.4+. To help maintaining a version you want and
        expect, a versioning scheme is used where:
        
        * the first three numbers indicate the version of Python 3.x from which the
          backport is done
        
        * a backport release number is provided after the last dot
        
        For example, ``3.4.0.0`` is the **first** release of ``singledispatch``
        compatible with the library found in Python **3.4.0**.
        
        A single exception from the 100% compatibility principle is that bugs
        fixed before releasing another minor Python 3.x.y version **will be
        included** in the backport releases done in the mean time. This rule
        applies to bugs only.
        
        
        Maintenance
        -----------
        
        This backport is maintained on BitBucket by Łukasz Langa, one of the
        members of the core CPython team:
        
        * `singledispatch Mercurial repository <https://bitbucket.org/ambv/singledispatch>`_
        
        * `singledispatch issue tracker <https://bitbucket.org/ambv/singledispatch/issues>`_
        
        
        Change Log
        ----------
        
        3.4.0.3
        ~~~~~~~
        
        Should now install flawlessly on PyPy as well. Thanks to Ryan Petrello
        for finding and fixing the ``setup.py`` issue.
        
        3.4.0.2
        ~~~~~~~
        
        Updated to the reference implementation as of 02-July-2013.
        
        * more predictable dispatch order when abstract base classes are in use:
          abstract base classes are now inserted into the MRO of the argument's
          class where their functionality is introduced, i.e. issubclass(cls,
          abc) returns True for the class itself but returns False for all its
          direct base classes. Implicit ABCs for a given class (either
          registered or inferred from the presence of a special method like
          __len__) are inserted directly after the last ABC explicitly listed in
          the MRO of said class. This also means there are less "ambiguous
          dispatch" exceptions raised.
        
        * better test coverage and improved docstrings
        
        3.4.0.1
        ~~~~~~~
        
        Updated to the reference implementation as of 31-May-2013.
        
        * better performance
        
        * fixed a corner case with PEP 435 enums
        
        * calls to `dispatch()` also cached
        
        * dispatching algorithm now now a module-level routine called `_find_impl()`
          with a simplified implementation and proper documentation
        
        * `dispatch()` now handles all caching-related activities
        
        * terminology more consistent: "overload" -> "implementation"
        
        3.4.0.0
        ~~~~~~~
        
        * the first public release compatible with 3.4.0
        
        
        Conversion Process
        ------------------
        
        This section is technical and should bother you only if you are
        wondering how this backport is produced. If the implementation details
        of this backport are not important for you, feel free to ignore the
        following content.
        
        ``singledispatch`` is converted using `six
        <http://pypi.python.org/pypi/six>`_ so that a single codebase can be
        used for all compatible Python versions.  Because a fully automatic
        conversion was not doable, I took the following branching approach:
        
        * the ``upstream`` branch holds unchanged files synchronized from the
          upstream CPython repository. The synchronization is currently done by
          manually copying the required code parts and stating from which
          CPython changeset they come from. The tests should pass on Python 3.4
          on this branch.
        
        * the ``default`` branch holds the manually translated version and this
          is where all tests are run for all supported Python versions using
          Tox.
        
Keywords: single dispatch generic functions singledispatch genericfunctions decorator backport
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules